hyperparameter optimization with customised environment

Hi, first question I have is that is it possible to run hyperparameter optimization using customised environment? If possible, what should be the file structure for train.py to recognize the environment. I have tried the following structure

│   train.py    
|   setup.py
│
└───gym_environment/
│   │   envs/
│   │   __init__.py

where __init__.py registers

    id='FullFilterEnv-v0',
    entry_point='gym_environment.envs:FullFilterEnv',
    max_episode_steps=10,

Then I run python train.py --algo td3 --env FullFilterEnv-v0 -n 50000 -optimize --n-trials 1000 --n-jobs 2 --sampler random --pruner median .
But the following error pops up ValueError: FullFilterEnv-v0 not found in gym registry, you maybe meant AntBulletEnv-v0?

Should I put the env into the yml file in hyperparams/ ? Perhaps a good example in the documents would be helpful.

Thanks a lot!

System Info

• stable-baselines3-0.8.0a0

[araffin]

run hyperparameter optimization using customised environment?

Yes, it is totally possible, in fact we are already doing that for pybullet envs. Best is to create a python package and import it in utils/import_envs.py. Otherwise, you can use --gym-packages package_name, see https://github.com/DLR-RM/rl-baselines3-zoo#minigrid-envs

Should I put the env into the yml file in hyperparams/

You should also do that too.

Related: https://github.com/araffin/rl-baselines-zoo/issues/29

[araffin]

Closing this as the original question was answered.